Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations100000
Missing cells0
Missing cells (%)0.0%
Duplicate rows45
Duplicate rows (%)< 0.1%
Total size in memory17.5 MiB
Average record size in memory184.0 B

Variable types

Categorical12
Numeric11

Alerts

Dataset has 45 (< 0.1%) duplicate rowsDuplicates
category is highly overall correlated with cost and 6 other fieldsHigh correlation
cost is highly overall correlated with category and 7 other fieldsHigh correlation
current_price is highly overall correlated with profit_margin and 3 other fieldsHigh correlation
discount_pct is highly overall correlated with ratioHigh correlation
gender is highly overall correlated with category and 4 other fieldsHigh correlation
has_extra_sizes is highly overall correlated with category and 4 other fieldsHigh correlation
main_color is highly overall correlated with category and 6 other fieldsHigh correlation
month is highly overall correlated with week_numberHigh correlation
productgroup is highly overall correlated with category and 3 other fieldsHigh correlation
profit_margin is highly overall correlated with cost and 4 other fieldsHigh correlation
promo1 is highly overall correlated with week_numberHigh correlation
ratio is highly overall correlated with discount_pctHigh correlation
regular_price is highly overall correlated with current_price and 3 other fieldsHigh correlation
sales is highly overall correlated with total_profitHigh correlation
sec_color is highly overall correlated with category and 4 other fieldsHigh correlation
style is highly overall correlated with category and 3 other fieldsHigh correlation
total_profit is highly overall correlated with current_price and 4 other fieldsHigh correlation
unit_profit is highly overall correlated with current_price and 3 other fieldsHigh correlation
week_number is highly overall correlated with month and 1 other fieldsHigh correlation
has_extra_sizes is highly imbalanced (53.1%)Imbalance
promo1 is highly imbalanced (66.5%)Imbalance
promo2 is highly imbalanced (95.5%)Imbalance
main_color is uniformly distributedUniform
discount_pct has 1490 (1.5%) zerosZeros

Reproduction

Analysis started2025-05-08 09:55:12.561540
Analysis finished2025-05-08 09:55:32.174361
Duration19.61 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

country
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
Germany
49400 
Austria
35140 
France
15460 

Length

Max length7
Median length7
Mean length6.8454
Min length6

Characters and Unicode

Total characters684540
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGermany
2nd rowGermany
3rd rowGermany
4th rowGermany
5th rowGermany

Common Values

ValueCountFrequency (%)
Germany 49400
49.4%
Austria 35140
35.1%
France 15460
 
15.5%

Length

2025-05-08T12:55:32.277274image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-08T12:55:32.388631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
germany 49400
49.4%
austria 35140
35.1%
france 15460
 
15.5%

Most occurring characters

ValueCountFrequency (%)
r 100000
14.6%
a 100000
14.6%
e 64860
9.5%
n 64860
9.5%
G 49400
7.2%
m 49400
7.2%
y 49400
7.2%
A 35140
 
5.1%
u 35140
 
5.1%
s 35140
 
5.1%
Other values (4) 101200
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 684540
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 100000
14.6%
a 100000
14.6%
e 64860
9.5%
n 64860
9.5%
G 49400
7.2%
m 49400
7.2%
y 49400
7.2%
A 35140
 
5.1%
u 35140
 
5.1%
s 35140
 
5.1%
Other values (4) 101200
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 684540
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 100000
14.6%
a 100000
14.6%
e 64860
9.5%
n 64860
9.5%
G 49400
7.2%
m 49400
7.2%
y 49400
7.2%
A 35140
 
5.1%
u 35140
 
5.1%
s 35140
 
5.1%
Other values (4) 101200
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 684540
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 100000
14.6%
a 100000
14.6%
e 64860
9.5%
n 64860
9.5%
G 49400
7.2%
m 49400
7.2%
y 49400
7.2%
A 35140
 
5.1%
u 35140
 
5.1%
s 35140
 
5.1%
Other values (4) 101200
14.8%

productgroup
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
SHOES
60000 
HARDWARE ACCESSORIES
20000 
SHORTS
10000 
SWEATSHIRTS
10000 

Length

Max length20
Median length5
Mean length8.7
Min length5

Characters and Unicode

Total characters870000
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSHOES
2nd rowSHORTS
3rd rowHARDWARE ACCESSORIES
4th rowSHOES
5th rowSHOES

Common Values

ValueCountFrequency (%)
SHOES 60000
60.0%
HARDWARE ACCESSORIES 20000
 
20.0%
SHORTS 10000
 
10.0%
SWEATSHIRTS 10000
 
10.0%

Length

2025-05-08T12:55:32.512242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-08T12:55:32.627665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
shoes 60000
50.0%
hardware 20000
 
16.7%
accessories 20000
 
16.7%
shorts 10000
 
8.3%
sweatshirts 10000
 
8.3%

Most occurring characters

ValueCountFrequency (%)
S 230000
26.4%
E 130000
14.9%
H 100000
11.5%
O 90000
 
10.3%
R 80000
 
9.2%
A 70000
 
8.0%
C 40000
 
4.6%
W 30000
 
3.4%
I 30000
 
3.4%
T 30000
 
3.4%
Other values (2) 40000
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 870000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 230000
26.4%
E 130000
14.9%
H 100000
11.5%
O 90000
 
10.3%
R 80000
 
9.2%
A 70000
 
8.0%
C 40000
 
4.6%
W 30000
 
3.4%
I 30000
 
3.4%
T 30000
 
3.4%
Other values (2) 40000
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 870000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 230000
26.4%
E 130000
14.9%
H 100000
11.5%
O 90000
 
10.3%
R 80000
 
9.2%
A 70000
 
8.0%
C 40000
 
4.6%
W 30000
 
3.4%
I 30000
 
3.4%
T 30000
 
3.4%
Other values (2) 40000
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 870000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 230000
26.4%
E 130000
14.9%
H 100000
11.5%
O 90000
 
10.3%
R 80000
 
9.2%
A 70000
 
8.0%
C 40000
 
4.6%
W 30000
 
3.4%
I 30000
 
3.4%
T 30000
 
3.4%
Other values (2) 40000
 
4.6%

category
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
TRAINING
30000 
RUNNING
20000 
FOOTBALL GENERIC
20000 
GOLF
10000 
RELAX CASUAL
10000 

Length

Max length16
Median length10
Mean length9.2
Min length4

Characters and Unicode

Total characters920000
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTRAINING
2nd rowTRAINING
3rd rowGOLF
4th rowRUNNING
5th rowRELAX CASUAL

Common Values

ValueCountFrequency (%)
TRAINING 30000
30.0%
RUNNING 20000
20.0%
FOOTBALL GENERIC 20000
20.0%
GOLF 10000
 
10.0%
RELAX CASUAL 10000
 
10.0%
INDOOR 10000
 
10.0%

Length

2025-05-08T12:55:32.759879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-08T12:55:32.884449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
training 30000
23.1%
running 20000
15.4%
football 20000
15.4%
generic 20000
15.4%
golf 10000
 
7.7%
relax 10000
 
7.7%
casual 10000
 
7.7%
indoor 10000
 
7.7%

Most occurring characters

ValueCountFrequency (%)
N 150000
16.3%
I 110000
12.0%
R 90000
9.8%
A 80000
8.7%
G 80000
8.7%
O 70000
7.6%
L 70000
7.6%
E 50000
 
5.4%
T 50000
 
5.4%
F 30000
 
3.3%
Other values (7) 140000
15.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 920000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 150000
16.3%
I 110000
12.0%
R 90000
9.8%
A 80000
8.7%
G 80000
8.7%
O 70000
7.6%
L 70000
7.6%
E 50000
 
5.4%
T 50000
 
5.4%
F 30000
 
3.3%
Other values (7) 140000
15.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 920000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 150000
16.3%
I 110000
12.0%
R 90000
9.8%
A 80000
8.7%
G 80000
8.7%
O 70000
7.6%
L 70000
7.6%
E 50000
 
5.4%
T 50000
 
5.4%
F 30000
 
3.3%
Other values (7) 140000
15.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 920000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 150000
16.3%
I 110000
12.0%
R 90000
9.8%
A 80000
8.7%
G 80000
8.7%
O 70000
7.6%
L 70000
7.6%
E 50000
 
5.4%
T 50000
 
5.4%
F 30000
 
3.3%
Other values (7) 140000
15.2%

style
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
regular
50000 
wide
30000 
slim
20000 

Length

Max length7
Median length5.5
Mean length5.5
Min length4

Characters and Unicode

Total characters550000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowslim
2nd rowregular
3rd rowregular
4th rowregular
5th rowregular

Common Values

ValueCountFrequency (%)
regular 50000
50.0%
wide 30000
30.0%
slim 20000
 
20.0%

Length

2025-05-08T12:55:33.024003image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-08T12:55:33.123140image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
regular 50000
50.0%
wide 30000
30.0%
slim 20000
 
20.0%

Most occurring characters

ValueCountFrequency (%)
r 100000
18.2%
e 80000
14.5%
l 70000
12.7%
g 50000
9.1%
u 50000
9.1%
a 50000
9.1%
i 50000
9.1%
w 30000
 
5.5%
d 30000
 
5.5%
s 20000
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 550000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 100000
18.2%
e 80000
14.5%
l 70000
12.7%
g 50000
9.1%
u 50000
9.1%
a 50000
9.1%
i 50000
9.1%
w 30000
 
5.5%
d 30000
 
5.5%
s 20000
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 550000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 100000
18.2%
e 80000
14.5%
l 70000
12.7%
g 50000
9.1%
u 50000
9.1%
a 50000
9.1%
i 50000
9.1%
w 30000
 
5.5%
d 30000
 
5.5%
s 20000
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 550000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 100000
18.2%
e 80000
14.5%
l 70000
12.7%
g 50000
9.1%
u 50000
9.1%
a 50000
9.1%
i 50000
9.1%
w 30000
 
5.5%
d 30000
 
5.5%
s 20000
 
3.6%

gender
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
women
70000 
kids
10000 
unisex
10000 
men
10000 

Length

Max length6
Median length5
Mean length4.8
Min length3

Characters and Unicode

Total characters480000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwomen
2nd rowwomen
3rd rowwomen
4th rowkids
5th rowwomen

Common Values

ValueCountFrequency (%)
women 70000
70.0%
kids 10000
 
10.0%
unisex 10000
 
10.0%
men 10000
 
10.0%

Length

2025-05-08T12:55:33.252459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-08T12:55:33.370961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
women 70000
70.0%
kids 10000
 
10.0%
unisex 10000
 
10.0%
men 10000
 
10.0%

Most occurring characters

ValueCountFrequency (%)
e 90000
18.8%
n 90000
18.8%
m 80000
16.7%
w 70000
14.6%
o 70000
14.6%
i 20000
 
4.2%
s 20000
 
4.2%
k 10000
 
2.1%
d 10000
 
2.1%
u 10000
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 480000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 90000
18.8%
n 90000
18.8%
m 80000
16.7%
w 70000
14.6%
o 70000
14.6%
i 20000
 
4.2%
s 20000
 
4.2%
k 10000
 
2.1%
d 10000
 
2.1%
u 10000
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 480000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 90000
18.8%
n 90000
18.8%
m 80000
16.7%
w 70000
14.6%
o 70000
14.6%
i 20000
 
4.2%
s 20000
 
4.2%
k 10000
 
2.1%
d 10000
 
2.1%
u 10000
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 480000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 90000
18.8%
n 90000
18.8%
m 80000
16.7%
w 70000
14.6%
o 70000
14.6%
i 20000
 
4.2%
s 20000
 
4.2%
k 10000
 
2.1%
d 10000
 
2.1%
u 10000
 
2.1%

main_color
Categorical

HIGH CORRELATION  UNIFORM 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
chocolate
10000 
darkkhaki
10000 
goldenrod
10000 
rosybrown
10000 
blueviolet
10000 
Other values (5)
50000 

Length

Max length12
Median length11
Mean length8.2
Min length4

Characters and Unicode

Total characters820000
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowchocolate
2nd rowdarkkhaki
3rd rowgoldenrod
4th rowrosybrown
5th rowblueviolet

Common Values

ValueCountFrequency (%)
chocolate 10000
10.0%
darkkhaki 10000
10.0%
goldenrod 10000
10.0%
rosybrown 10000
10.0%
blueviolet 10000
10.0%
steelblue 10000
10.0%
brown 10000
10.0%
lightskyblue 10000
10.0%
silver 10000
10.0%
gray 10000
10.0%

Length

2025-05-08T12:55:33.523654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-08T12:55:33.674692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
chocolate 10000
10.0%
darkkhaki 10000
10.0%
goldenrod 10000
10.0%
rosybrown 10000
10.0%
blueviolet 10000
10.0%
steelblue 10000
10.0%
brown 10000
10.0%
lightskyblue 10000
10.0%
silver 10000
10.0%
gray 10000
10.0%

Most occurring characters

ValueCountFrequency (%)
l 90000
 
11.0%
e 90000
 
11.0%
o 80000
 
9.8%
r 70000
 
8.5%
b 50000
 
6.1%
k 40000
 
4.9%
a 40000
 
4.9%
t 40000
 
4.9%
i 40000
 
4.9%
s 40000
 
4.9%
Other values (9) 240000
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 820000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 90000
 
11.0%
e 90000
 
11.0%
o 80000
 
9.8%
r 70000
 
8.5%
b 50000
 
6.1%
k 40000
 
4.9%
a 40000
 
4.9%
t 40000
 
4.9%
i 40000
 
4.9%
s 40000
 
4.9%
Other values (9) 240000
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 820000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 90000
 
11.0%
e 90000
 
11.0%
o 80000
 
9.8%
r 70000
 
8.5%
b 50000
 
6.1%
k 40000
 
4.9%
a 40000
 
4.9%
t 40000
 
4.9%
i 40000
 
4.9%
s 40000
 
4.9%
Other values (9) 240000
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 820000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 90000
 
11.0%
e 90000
 
11.0%
o 80000
 
9.8%
r 70000
 
8.5%
b 50000
 
6.1%
k 40000
 
4.9%
a 40000
 
4.9%
t 40000
 
4.9%
i 40000
 
4.9%
s 40000
 
4.9%
Other values (9) 240000
29.3%

sec_color
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
rosybrown
40000 
lavender
30000 
lightblue
30000 

Length

Max length9
Median length9
Mean length8.7
Min length8

Characters and Unicode

Total characters870000
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlavender
2nd rowlavender
3rd rowlavender
4th rowlightblue
5th rowlightblue

Common Values

ValueCountFrequency (%)
rosybrown 40000
40.0%
lavender 30000
30.0%
lightblue 30000
30.0%

Length

2025-05-08T12:55:33.854676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-08T12:55:33.976091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
rosybrown 40000
40.0%
lavender 30000
30.0%
lightblue 30000
30.0%

Most occurring characters

ValueCountFrequency (%)
r 110000
12.6%
l 90000
10.3%
e 90000
10.3%
o 80000
 
9.2%
b 70000
 
8.0%
n 70000
 
8.0%
s 40000
 
4.6%
y 40000
 
4.6%
w 40000
 
4.6%
g 30000
 
3.4%
Other values (7) 210000
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 870000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 110000
12.6%
l 90000
10.3%
e 90000
10.3%
o 80000
 
9.2%
b 70000
 
8.0%
n 70000
 
8.0%
s 40000
 
4.6%
y 40000
 
4.6%
w 40000
 
4.6%
g 30000
 
3.4%
Other values (7) 210000
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 870000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 110000
12.6%
l 90000
10.3%
e 90000
10.3%
o 80000
 
9.2%
b 70000
 
8.0%
n 70000
 
8.0%
s 40000
 
4.6%
y 40000
 
4.6%
w 40000
 
4.6%
g 30000
 
3.4%
Other values (7) 210000
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 870000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 110000
12.6%
l 90000
10.3%
e 90000
10.3%
o 80000
 
9.2%
b 70000
 
8.0%
n 70000
 
8.0%
s 40000
 
4.6%
y 40000
 
4.6%
w 40000
 
4.6%
g 30000
 
3.4%
Other values (7) 210000
24.1%

has_extra_sizes
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
1
90000 
0
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 90000
90.0%
0 10000
 
10.0%

Length

2025-05-08T12:55:34.111697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-08T12:55:34.227214image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 90000
90.0%
0 10000
 
10.0%

Most occurring characters

ValueCountFrequency (%)
1 90000
90.0%
0 10000
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 90000
90.0%
0 10000
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 90000
90.0%
0 10000
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 90000
90.0%
0 10000
 
10.0%

year
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
2015
42790 
2016
41830 
2017
14600 
2014
 
780

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters400000
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2015 42790
42.8%
2016 41830
41.8%
2017 14600
 
14.6%
2014 780
 
0.8%

Length

2025-05-08T12:55:34.345624image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-08T12:55:34.464428image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2015 42790
42.8%
2016 41830
41.8%
2017 14600
 
14.6%
2014 780
 
0.8%

Most occurring characters

ValueCountFrequency (%)
2 100000
25.0%
0 100000
25.0%
1 100000
25.0%
5 42790
10.7%
6 41830
10.5%
7 14600
 
3.6%
4 780
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 400000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 100000
25.0%
0 100000
25.0%
1 100000
25.0%
5 42790
10.7%
6 41830
10.5%
7 14600
 
3.6%
4 780
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 400000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 100000
25.0%
0 100000
25.0%
1 100000
25.0%
5 42790
10.7%
6 41830
10.5%
7 14600
 
3.6%
4 780
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 400000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 100000
25.0%
0 100000
25.0%
1 100000
25.0%
5 42790
10.7%
6 41830
10.5%
7 14600
 
3.6%
4 780
 
0.2%

month
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9056
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-05-08T12:55:34.569381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5312623
Coefficient of variation (CV)0.59795148
Kurtosis-1.2258352
Mean5.9056
Median Absolute Deviation (MAD)3
Skewness0.23810075
Sum590560
Variance12.469813
MonotonicityNot monotonic
2025-05-08T12:55:34.673403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 11730
11.7%
4 10680
10.7%
3 10190
10.2%
2 9980
10.0%
5 8540
8.5%
11 7550
7.5%
10 7100
7.1%
12 7080
7.1%
8 7080
7.1%
7 7010
7.0%
Other values (2) 13060
13.1%
ValueCountFrequency (%)
1 11730
11.7%
2 9980
10.0%
3 10190
10.2%
4 10680
10.7%
5 8540
8.5%
6 6460
6.5%
7 7010
7.0%
8 7080
7.1%
9 6600
6.6%
10 7100
7.1%
ValueCountFrequency (%)
12 7080
7.1%
11 7550
7.5%
10 7100
7.1%
9 6600
6.6%
8 7080
7.1%
7 7010
7.0%
6 6460
6.5%
5 8540
8.5%
4 10680
10.7%
3 10190
10.2%

week_number
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.3435
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-05-08T12:55:34.795753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q110.75
median22
Q338
95-th percentile50
Maximum53
Range52
Interquartile range (IQR)27.25

Descriptive statistics

Standard deviation15.614882
Coefficient of variation (CV)0.64143949
Kurtosis-1.2276995
Mean24.3435
Median Absolute Deviation (MAD)13
Skewness0.2332201
Sum2434350
Variance243.82455
MonotonicityNot monotonic
2025-05-08T12:55:34.933254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 2770
 
2.8%
5 2700
 
2.7%
4 2650
 
2.6%
15 2630
 
2.6%
17 2620
 
2.6%
9 2580
 
2.6%
16 2510
 
2.5%
2 2450
 
2.5%
6 2450
 
2.5%
7 2420
 
2.4%
Other values (43) 74220
74.2%
ValueCountFrequency (%)
1 2260
2.3%
2 2450
2.5%
3 2770
2.8%
4 2650
2.6%
5 2700
2.7%
6 2450
2.5%
7 2420
2.4%
8 2410
2.4%
9 2580
2.6%
10 2310
2.3%
ValueCountFrequency (%)
53 740
 
0.7%
52 2360
2.4%
51 1790
1.8%
50 1520
1.5%
49 1490
1.5%
48 1690
1.7%
47 1960
2.0%
46 1440
1.4%
45 1720
1.7%
44 1520
1.5%

regular_price
Real number (ℝ)

HIGH CORRELATION 

Distinct123
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.3912
Minimum3.95
Maximum197.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-05-08T12:55:35.064833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3.95
5-th percentile6.95
Q125.95
median40.95
Q379.95
95-th percentile120.95
Maximum197.95
Range194
Interquartile range (IQR)54

Descriptive statistics

Standard deviation35.272128
Coefficient of variation (CV)0.67324527
Kurtosis0.32235243
Mean52.3912
Median Absolute Deviation (MAD)20
Skewness0.90371157
Sum5239120
Variance1244.123
MonotonicityNot monotonic
2025-05-08T12:55:35.206905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.95 3620
 
3.6%
29.95 3160
 
3.2%
30.95 3120
 
3.1%
23.95 3110
 
3.1%
62.95 2690
 
2.7%
25.95 2540
 
2.5%
44.95 2420
 
2.4%
20.95 2330
 
2.3%
3.95 2090
 
2.1%
83.95 1920
 
1.9%
Other values (113) 73000
73.0%
ValueCountFrequency (%)
3.95 2090
2.1%
4.95 570
 
0.6%
5.95 1270
1.3%
6.95 1330
1.3%
7.95 170
 
0.2%
8.95 680
 
0.7%
9.95 510
 
0.5%
10.95 800
 
0.8%
11.95 130
 
0.1%
12.95 190
 
0.2%
ValueCountFrequency (%)
197.95 120
 
0.1%
195.95 160
 
0.2%
153.95 850
0.9%
150.95 150
 
0.1%
141.95 90
 
0.1%
139.95 240
 
0.2%
136.95 200
 
0.2%
135.95 270
 
0.3%
134.95 150
 
0.1%
132.95 490
0.5%

current_price
Real number (ℝ)

HIGH CORRELATION 

Distinct141
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.2908
Minimum1.95
Maximum195.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-05-08T12:55:35.345099image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.95
5-th percentile3.95
Q111.95
median20.95
Q337.95
95-th percentile74.95
Maximum195.95
Range194
Interquartile range (IQR)26

Descriptive statistics

Standard deviation22.578343
Coefficient of variation (CV)0.79808074
Kurtosis2.9168272
Mean28.2908
Median Absolute Deviation (MAD)11
Skewness1.5474818
Sum2829080
Variance509.78155
MonotonicityNot monotonic
2025-05-08T12:55:35.487592image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.95 3660
 
3.7%
9.95 3360
 
3.4%
11.95 3230
 
3.2%
13.95 3130
 
3.1%
17.95 2930
 
2.9%
12.95 2920
 
2.9%
16.95 2890
 
2.9%
15.95 2720
 
2.7%
7.95 2670
 
2.7%
14.95 2520
 
2.5%
Other values (131) 69970
70.0%
ValueCountFrequency (%)
1.95 1730
1.7%
2.95 1990
2.0%
3.95 1420
 
1.4%
4.95 1650
1.7%
5.95 1960
2.0%
6.95 2160
2.2%
7.95 2670
2.7%
8.95 3660
3.7%
9.95 3360
3.4%
10.95 2400
2.4%
ValueCountFrequency (%)
195.95 10
< 0.1%
178.95 10
< 0.1%
154.95 10
< 0.1%
152.95 20
< 0.1%
145.95 20
< 0.1%
144.95 10
< 0.1%
141.95 10
< 0.1%
140.95 10
< 0.1%
136.95 10
< 0.1%
135.95 10
< 0.1%

ratio
Real number (ℝ)

HIGH CORRELATION 

Distinct2722
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54564586
Minimum0.29648241
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-05-08T12:55:35.640893image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.29648241
5-th percentile0.30283224
Q10.35483871
median0.52504358
Q30.69924812
95-th percentile0.88868275
Maximum1
Range0.70351759
Interquartile range (IQR)0.34440941

Descriptive statistics

Standard deviation0.19436278
Coefficient of variation (CV)0.35620682
Kurtosis-0.9113374
Mean0.54564586
Median Absolute Deviation (MAD)0.17124714
Skewness0.39778993
Sum54564.586
Variance0.03777689
MonotonicityNot monotonic
2025-05-08T12:55:35.780972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1490
 
1.5%
0.4936708861 1140
 
1.1%
0.3103448276 830
 
0.8%
0.332096475 820
 
0.8%
0.3214862682 760
 
0.8%
0.2988313856 730
 
0.7%
0.746835443 720
 
0.7%
0.3319415449 690
 
0.7%
0.3317422434 570
 
0.6%
0.3548387097 510
 
0.5%
Other values (2712) 91740
91.7%
ValueCountFrequency (%)
0.2964824121 120
 
0.1%
0.298245614 230
 
0.2%
0.2988313856 730
0.7%
0.2991239049 140
 
0.1%
0.2992992993 160
 
0.2%
0.2994161802 40
 
< 0.1%
0.2994996426 310
0.3%
0.2995622264 50
 
0.1%
0.2996108949 60
 
0.1%
0.2996498249 70
 
0.1%
ValueCountFrequency (%)
1 1490
1.5%
0.9920603414 10
 
< 0.1%
0.9917321207 10
 
< 0.1%
0.9915218313 10
 
< 0.1%
0.9904716532 10
 
< 0.1%
0.9899949975 10
 
< 0.1%
0.9890049478 10
 
< 0.1%
0.9880881477 10
 
< 0.1%
0.9857040743 20
 
< 0.1%
0.9841206828 10
 
< 0.1%

discount_pct
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2722
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.45435414
Minimum0
Maximum0.70351759
Zeros1490
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-05-08T12:55:35.928848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.11131725
Q10.30075188
median0.47495642
Q30.64516129
95-th percentile0.69716776
Maximum0.70351759
Range0.70351759
Interquartile range (IQR)0.34440941

Descriptive statistics

Standard deviation0.19436278
Coefficient of variation (CV)0.42777816
Kurtosis-0.9113374
Mean0.45435414
Median Absolute Deviation (MAD)0.17124714
Skewness-0.39778993
Sum45435.414
Variance0.03777689
MonotonicityNot monotonic
2025-05-08T12:55:36.271620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1490
 
1.5%
0.5063291139 1140
 
1.1%
0.6896551724 830
 
0.8%
0.667903525 820
 
0.8%
0.6785137318 760
 
0.8%
0.7011686144 730
 
0.7%
0.253164557 720
 
0.7%
0.6680584551 690
 
0.7%
0.6682577566 570
 
0.6%
0.6451612903 510
 
0.5%
Other values (2712) 91740
91.7%
ValueCountFrequency (%)
0 1490
1.5%
0.007939658595 10
 
< 0.1%
0.008267879289 10
 
< 0.1%
0.008478168716 10
 
< 0.1%
0.009528346832 10
 
< 0.1%
0.0100050025 10
 
< 0.1%
0.01099505223 10
 
< 0.1%
0.01191185229 10
 
< 0.1%
0.01429592566 20
 
< 0.1%
0.01587931719 10
 
< 0.1%
ValueCountFrequency (%)
0.7035175879 120
 
0.1%
0.701754386 230
 
0.2%
0.7011686144 730
0.7%
0.7008760951 140
 
0.1%
0.7007007007 160
 
0.2%
0.7005838198 40
 
< 0.1%
0.7005003574 310
0.3%
0.7004377736 50
 
0.1%
0.7003891051 60
 
0.1%
0.7003501751 70
 
0.1%

cost
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.517
Minimum1.29
Maximum13.29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-05-08T12:55:36.412909image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.29
5-th percentile1.29
Q12.29
median6.95
Q39.6
95-th percentile13.29
Maximum13.29
Range12
Interquartile range (IQR)7.31

Descriptive statistics

Standard deviation3.9147279
Coefficient of variation (CV)0.60069478
Kurtosis-1.2872918
Mean6.517
Median Absolute Deviation (MAD)2.85
Skewness0.099353368
Sum651700
Variance15.325094
MonotonicityNot monotonic
2025-05-08T12:55:36.517776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
13.29 10000
10.0%
2.29 10000
10.0%
1.7 10000
10.0%
9 10000
10.0%
9.6 10000
10.0%
4.2 10000
10.0%
9.9 10000
10.0%
5.2 10000
10.0%
1.29 10000
10.0%
8.7 10000
10.0%
ValueCountFrequency (%)
1.29 10000
10.0%
1.7 10000
10.0%
2.29 10000
10.0%
4.2 10000
10.0%
5.2 10000
10.0%
8.7 10000
10.0%
9 10000
10.0%
9.6 10000
10.0%
9.9 10000
10.0%
13.29 10000
10.0%
ValueCountFrequency (%)
13.29 10000
10.0%
9.9 10000
10.0%
9.6 10000
10.0%
9 10000
10.0%
8.7 10000
10.0%
5.2 10000
10.0%
4.2 10000
10.0%
2.29 10000
10.0%
1.7 10000
10.0%
1.29 10000
10.0%

sales
Real number (ℝ)

HIGH CORRELATION 

Distinct476
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.7818
Minimum1
Maximum898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-05-08T12:55:36.640293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q110
median26
Q364
95-th percentile216
Maximum898
Range897
Interquartile range (IQR)54

Descriptive statistics

Standard deviation87.934743
Coefficient of variation (CV)1.5486431
Kurtosis20.657374
Mean56.7818
Median Absolute Deviation (MAD)20
Skewness3.8588957
Sum5678180
Variance7732.5191
MonotonicityNot monotonic
2025-05-08T12:55:36.786670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 3080
 
3.1%
1 3060
 
3.1%
3 2950
 
2.9%
4 2800
 
2.8%
5 2680
 
2.7%
6 2670
 
2.7%
8 2380
 
2.4%
7 2380
 
2.4%
9 2160
 
2.2%
11 2130
 
2.1%
Other values (466) 73710
73.7%
ValueCountFrequency (%)
1 3060
3.1%
2 3080
3.1%
3 2950
2.9%
4 2800
2.8%
5 2680
2.7%
6 2670
2.7%
7 2380
2.4%
8 2380
2.4%
9 2160
2.2%
10 1940
1.9%
ValueCountFrequency (%)
898 10
< 0.1%
883 10
< 0.1%
881 10
< 0.1%
852 10
< 0.1%
841 10
< 0.1%
827 10
< 0.1%
819 10
< 0.1%
818 20
< 0.1%
797 10
< 0.1%
796 10
< 0.1%

unit_profit
Real number (ℝ)

HIGH CORRELATION 

Distinct922
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.7738
Minimum-11.34
Maximum194.66
Zeros0
Zeros (%)0.0%
Negative10811
Negative (%)10.8%
Memory size781.4 KiB
2025-05-08T12:55:36.921000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-11.34
5-th percentile-3.34
Q15.66
median15.35
Q331.66
95-th percentile68.75
Maximum194.66
Range206
Interquartile range (IQR)26

Descriptive statistics

Standard deviation22.915206
Coefficient of variation (CV)1.0524211
Kurtosis2.747965
Mean21.7738
Median Absolute Deviation (MAD)11.69
Skewness1.4797389
Sum2177380
Variance525.10665
MonotonicityNot monotonic
2025-05-08T12:55:37.092392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.66 796
 
0.8%
11.66 785
 
0.8%
8.66 766
 
0.8%
12.66 764
 
0.8%
9.66 753
 
0.8%
15.66 739
 
0.7%
14.66 729
 
0.7%
13.66 723
 
0.7%
4.66 705
 
0.7%
7.66 702
 
0.7%
Other values (912) 92538
92.5%
ValueCountFrequency (%)
-11.34 173
0.2%
-10.34 199
0.2%
-9.34 142
0.1%
-8.34 165
0.2%
-7.95 173
0.2%
-7.65 173
0.2%
-7.34 196
0.2%
-7.05 173
0.2%
-6.95 199
0.2%
-6.75 173
0.2%
ValueCountFrequency (%)
194.66 1
< 0.1%
194.25 1
< 0.1%
193.66 1
< 0.1%
191.75 1
< 0.1%
190.75 1
< 0.1%
187.25 1
< 0.1%
186.95 1
< 0.1%
186.35 1
< 0.1%
186.05 1
< 0.1%
182.66 1
< 0.1%

total_profit
Real number (ℝ)

HIGH CORRELATION 

Distinct35945
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean997.09622
Minimum-9026.64
Maximum57743.06
Zeros0
Zeros (%)0.0%
Negative10811
Negative (%)10.8%
Memory size781.4 KiB
2025-05-08T12:55:37.234288image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-9026.64
5-th percentile-95.42
Q173.3
median316.54
Q3997.5375
95-th percentile4347.5275
Maximum57743.06
Range66769.7
Interquartile range (IQR)924.2375

Descriptive statistics

Standard deviation2258.2682
Coefficient of variation (CV)2.2648448
Kurtosis73.025618
Mean997.09622
Median Absolute Deviation (MAD)297.9
Skewness6.4812423
Sum99709622
Variance5099775.2
MonotonicityNot monotonic
2025-05-08T12:55:37.392184image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
123.75 66
 
0.1%
78.75 60
 
0.1%
56.25 60
 
0.1%
213.75 57
 
0.1%
173.25 56
 
0.1%
146.25 56
 
0.1%
94.5 54
 
0.1%
57.75 52
 
0.1%
63 50
 
0.1%
113.75 49
 
< 0.1%
Other values (35935) 99440
99.4%
ValueCountFrequency (%)
-9026.64 1
< 0.1%
-7889.42 1
< 0.1%
-7444.8 1
< 0.1%
-6505.2 1
< 0.1%
-6328.2 1
< 0.1%
-6089.4 1
< 0.1%
-6007.54 1
< 0.1%
-5715.36 1
< 0.1%
-5611.8 1
< 0.1%
-5585.74 1
< 0.1%
ValueCountFrequency (%)
57743.06 1
< 0.1%
57398.25 1
< 0.1%
56902.06 1
< 0.1%
55295.75 1
< 0.1%
54454.75 1
< 0.1%
51511.25 1
< 0.1%
51258.95 1
< 0.1%
50754.35 1
< 0.1%
50502.05 1
< 0.1%
47651.06 1
< 0.1%

profit_margin
Real number (ℝ)

HIGH CORRELATION 

Distinct1410
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52636619
Minimum-5.8153846
Maximum0.99341669
Zeros0
Zeros (%)0.0%
Negative10811
Negative (%)10.8%
Memory size781.4 KiB
2025-05-08T12:55:37.558214image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-5.8153846
5-th percentile-0.61344538
Q10.44846797
median0.74965229
Q30.88924388
95-th percentile0.96527068
Maximum0.99341669
Range6.8088013
Interquartile range (IQR)0.44077591

Descriptive statistics

Standard deviation0.70144356
Coefficient of variation (CV)1.3326152
Kurtosis23.922473
Mean0.52636619
Median Absolute Deviation (MAD)0.1726778
Skewness-4.2143305
Sum52636.619
Variance0.49202307
MonotonicityNot monotonic
2025-05-08T12:55:37.710374image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.4849162011 366
 
0.4%
0.8100558659 366
 
0.4%
-0.005586592179 366
 
0.4%
-0.07262569832 366
 
0.4%
0.530726257 366
 
0.4%
-0.1061452514 366
 
0.4%
0.4189944134 366
 
0.4%
0.8558659218 366
 
0.4%
0.02793296089 366
 
0.4%
0.7441340782 366
 
0.4%
Other values (1400) 96340
96.3%
ValueCountFrequency (%)
-5.815384615 173
0.2%
-4.076923077 173
0.2%
-3.923076923 173
0.2%
-3.615384615 173
0.2%
-3.505084746 199
0.2%
-3.461538462 173
0.2%
-2.364556962 142
0.1%
-2.355932203 199
0.2%
-2.254237288 199
0.2%
-2.050847458 199
0.2%
ValueCountFrequency (%)
0.9934166879 1
< 0.1%
0.9927912825 1
< 0.1%
0.9916747338 1
< 0.1%
0.9915658712 2
< 0.1%
0.9913243174 1
< 0.1%
0.9911613566 2
< 0.1%
0.9911003794 1
< 0.1%
0.9909122931 1
< 0.1%
0.9908478184 1
< 0.1%
0.9905805038 1
< 0.1%

promo1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
0
93810 
1
 
6190

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 93810
93.8%
1 6190
 
6.2%

Length

2025-05-08T12:55:37.846492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-08T12:55:37.946507image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 93810
93.8%
1 6190
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 93810
93.8%
1 6190
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 93810
93.8%
1 6190
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 93810
93.8%
1 6190
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 93810
93.8%
1 6190
 
6.2%

promo2
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
0
99510 
1
 
490

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 99510
99.5%
1 490
 
0.5%

Length

2025-05-08T12:55:38.049931image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-08T12:55:38.145200image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 99510
99.5%
1 490
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 99510
99.5%
1 490
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 99510
99.5%
1 490
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 99510
99.5%
1 490
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 99510
99.5%
1 490
 
0.5%

label
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
0
86072 
1
13928 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters100000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 86072
86.1%
1 13928
 
13.9%

Length

2025-05-08T12:55:38.242278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-08T12:55:38.332612image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 86072
86.1%
1 13928
 
13.9%

Most occurring characters

ValueCountFrequency (%)
0 86072
86.1%
1 13928
 
13.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 86072
86.1%
1 13928
 
13.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 86072
86.1%
1 13928
 
13.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 86072
86.1%
1 13928
 
13.9%

Interactions

2025-05-08T12:55:29.891912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:18.122080image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:19.247371image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:20.332962image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:21.500525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:22.817186image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:23.976774image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:25.175765image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:26.322073image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:27.579724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:28.725267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:29.996978image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:18.240083image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:19.343085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:20.434450image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:21.601571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:22.921352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:24.087158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:25.268834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:26.428918image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:27.684690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:28.827117image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:30.095979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:18.333514image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:19.437047image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:20.535597image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:21.715530image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:23.022336image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:24.200416image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:25.370417image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:26.538651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:27.787147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:28.929389image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:30.208070image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:18.439139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:19.541551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:20.644025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:21.827150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:23.135563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:24.314985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:25.480238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:26.649673image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:27.898284image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:29.045856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:30.318467image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:18.555139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:19.645414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:20.755195image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:21.936609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:23.245082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:24.428269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:25.600664image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:26.896871image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:28.009945image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:29.159347image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:30.432311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:18.660048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:19.746225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:20.864720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:22.045848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:23.350305image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:24.540312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:25.706310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:26.998491image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:28.120115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:29.265291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:30.547999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:18.767022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:19.848528image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:20.975321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:22.158928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:23.462113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:24.656628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:25.810144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:27.095366image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:28.234137image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:29.373466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:30.650390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:18.857669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:19.945484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:21.078528image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:22.406044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:23.562963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:24.757465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:25.911981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:27.189917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:28.327715image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:29.474354image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:30.744648image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:18.951638image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:20.037085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:21.177998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:22.502950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:23.657273image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:24.853097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:26.008831image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:27.280509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:28.421159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:29.573986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:30.839844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:19.040284image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:20.127036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:21.274884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:22.598683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:23.757472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:24.950457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:26.105586image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:27.370294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:28.517425image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:29.670789image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:30.952161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:19.146095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:20.231420image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:21.387720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:22.709204image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:23.864446image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:25.058652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:26.212891image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:27.477096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:28.623627image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-08T12:55:29.783826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-05-08T12:55:38.420672image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
categorycostcountrycurrent_pricediscount_pctgenderhas_extra_sizeslabelmain_colormonthproductgroupprofit_marginpromo1promo2ratioregular_pricesalessec_colorstyletotal_profitunit_profitweek_numberyear
category1.0000.6990.0000.0000.0000.6900.6670.0051.0000.0000.6380.1170.0000.0000.0000.0000.0000.7950.5890.0120.0600.0000.000
cost0.6991.0000.0000.0000.0000.7241.0000.0001.0000.0000.816-0.6590.0000.0000.0000.0000.0000.7820.876-0.226-0.2250.0000.000
country0.0000.0001.0000.0760.0430.0000.0000.0100.0000.0320.0000.0320.0080.1640.0430.1750.0260.0000.0000.0190.0770.0280.014
current_price0.0000.0000.0761.000-0.3720.0000.0000.1810.000-0.1400.0000.7060.0690.0290.3720.885-0.1780.0000.0000.5920.966-0.1270.036
discount_pct0.0000.0000.043-0.3721.0000.0000.0000.4620.0000.3390.000-0.2560.1620.044-1.0000.0710.4350.0000.0000.042-0.3630.3120.065
gender0.6900.7240.0000.0000.0001.0001.0000.0001.0000.0000.3090.1230.0000.0000.0000.0000.0000.5460.4830.0080.0680.0000.000
has_extra_sizes0.6671.0000.0000.0000.0001.0001.0000.0001.0000.0000.2720.1410.0000.0000.0000.0000.0000.4080.5090.0110.0700.0000.000
label0.0050.0000.0100.1810.4620.0000.0001.0000.0030.3340.0000.0920.0640.0200.4620.0220.0990.0000.0000.0150.1820.3370.039
main_color1.0001.0000.0000.0000.0001.0001.0000.0031.0000.0001.0000.1630.0000.0000.0000.0000.0001.0001.0000.0200.0750.0000.000
month0.0000.0000.032-0.1400.3390.0000.0000.3340.0001.0000.000-0.0990.3900.091-0.3390.0060.2010.0000.0000.038-0.1370.9120.309
productgroup0.6380.8160.0000.0000.0000.3090.2720.0001.0000.0001.0000.1900.0000.0000.0000.0000.0000.5530.4940.0260.1170.0000.000
profit_margin0.117-0.6590.0320.706-0.2560.1230.1410.0920.163-0.0990.1901.0000.0400.0180.2560.629-0.1230.0760.0870.5860.844-0.0890.014
promo10.0000.0000.0080.0690.1620.0000.0000.0640.0000.3900.0000.0401.0000.0470.1620.0160.1260.0000.0000.1260.0700.5210.358
promo20.0000.0000.1640.0290.0440.0000.0000.0200.0000.0910.0000.0180.0471.0000.0440.0280.0160.0000.0000.0100.0250.0880.047
ratio0.0000.0000.0430.372-1.0000.0000.0000.4620.000-0.3390.0000.2560.1620.0441.000-0.071-0.4350.0000.000-0.0420.363-0.3120.065
regular_price0.0000.0000.1750.8850.0710.0000.0000.0220.0000.0060.0000.6290.0160.028-0.0711.0000.0110.0000.0000.6530.8540.0070.020
sales0.0000.0000.026-0.1780.4350.0000.0000.0990.0000.2010.000-0.1230.1260.016-0.4350.0111.0000.0000.0000.542-0.1740.2010.052
sec_color0.7950.7820.0000.0000.0000.5460.4080.0001.0000.0000.5530.0760.0000.0000.0000.0000.0001.0000.4000.0090.0330.0000.000
style0.5890.8760.0000.0000.0000.4830.5090.0001.0000.0000.4940.0870.0000.0000.0000.0000.0000.4001.0000.0160.0050.0000.000
total_profit0.012-0.2260.0190.5920.0420.0080.0110.0150.0200.0380.0260.5860.1260.010-0.0420.6530.5420.0090.0161.0000.6330.0500.041
unit_profit0.060-0.2250.0770.966-0.3630.0680.0700.1820.075-0.1370.1170.8440.0700.0250.3630.854-0.1740.0330.0050.6331.000-0.1240.030
week_number0.0000.0000.028-0.1270.3120.0000.0000.3370.0000.9120.000-0.0890.5210.088-0.3120.0070.2010.0000.0000.050-0.1241.0000.292
year0.0000.0000.0140.0360.0650.0000.0000.0390.0000.3090.0000.0140.3580.0470.0650.0200.0520.0000.0000.0410.0300.2921.000

Missing values

2025-05-08T12:55:31.341135image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-08T12:55:31.789330image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

countryproductgroupcategorystylegendermain_colorsec_colorhas_extra_sizesyearmonthweek_numberregular_pricecurrent_priceratiodiscount_pctcostsalesunit_profittotal_profitprofit_marginpromo1promo2label
0GermanySHOESTRAININGslimwomenchocolatelavender120163125.953.950.6638660.33613413.2928-9.34-261.52-2.364557000
1GermanySHORTSTRAININGregularwomendarkkhakilavender120163125.953.950.6638660.3361342.29281.6646.480.420253000
2GermanyHARDWARE ACCESSORIESGOLFregularwomengoldenrodlavender120163125.953.950.6638660.3361341.70282.2563.000.569620000
3GermanySHOESRUNNINGregularkidsrosybrownlightblue120163125.953.950.6638660.3361349.0028-5.05-141.40-1.278481000
4GermanySHOESRELAX CASUALregularwomenbluevioletlightblue120163125.953.950.6638660.3361349.6028-5.65-158.20-1.430380000
5GermanySWEATSHIRTSTRAININGwidewomensteelbluelightblue120163125.953.950.6638660.3361344.2028-0.25-7.00-0.063291001
6GermanySHOESFOOTBALL GENERICwideunisexbrownrosybrown020163125.953.950.6638660.3361349.9028-5.95-166.60-1.506329000
7GermanySHOESINDOORwidewomenlightskybluerosybrown120163125.953.950.6638660.3361345.2028-1.25-35.00-0.316456001
8GermanyHARDWARE ACCESSORIESRUNNINGslimwomensilverrosybrown120163125.953.950.6638660.3361341.29282.6674.480.673418000
9GermanySHOESFOOTBALL GENERICregularmengrayrosybrown120163125.953.950.6638660.3361348.7028-4.75-133.00-1.202532001
countryproductgroupcategorystylegendermain_colorsec_colorhas_extra_sizesyearmonthweek_numberregular_pricecurrent_priceratiodiscount_pctcostsalesunit_profittotal_profitprofit_marginpromo1promo2label
99990GermanySHOESTRAININGslimwomenchocolatelavender1201662557.9526.950.4650560.53494413.2922713.663100.820.506865000
99991GermanySHORTSTRAININGregularwomendarkkhakilavender1201662557.9526.950.4650560.5349442.2922724.665597.820.915028000
99992GermanyHARDWARE ACCESSORIESGOLFregularwomengoldenrodlavender1201662557.9526.950.4650560.5349441.7022725.255731.750.936920000
99993GermanySHOESRUNNINGregularkidsrosybrownlightblue1201662557.9526.950.4650560.5349449.0022717.954074.650.666048000
99994GermanySHOESRELAX CASUALregularwomenbluevioletlightblue1201662557.9526.950.4650560.5349449.6022717.353938.450.643785000
99995GermanySWEATSHIRTSTRAININGwidewomensteelbluelightblue1201662557.9526.950.4650560.5349444.2022722.755164.250.844156000
99996GermanySHOESFOOTBALL GENERICwideunisexbrownrosybrown0201662557.9526.950.4650560.5349449.9022717.053870.350.632653000
99997GermanySHOESINDOORwidewomenlightskybluerosybrown1201662557.9526.950.4650560.5349445.2022721.754937.250.807050000
99998GermanyHARDWARE ACCESSORIESRUNNINGslimwomensilverrosybrown1201662557.9526.950.4650560.5349441.2922725.665824.820.952134000
99999GermanySHOESFOOTBALL GENERICregularmengrayrosybrown1201662557.9526.950.4650560.5349448.7022718.254142.750.677180000

Duplicate rows

Most frequently occurring

countryproductgroupcategorystylegendermain_colorsec_colorhas_extra_sizesyearmonthweek_numberregular_pricecurrent_priceratiodiscount_pctcostsalesunit_profittotal_profitprofit_marginpromo1promo2label# duplicates
0AustriaHARDWARE ACCESSORIESGOLFregularwomengoldenrodlavender120167273.951.950.4936710.5063291.7080.252.000.1282050002
1AustriaHARDWARE ACCESSORIESRUNNINGslimwomensilverrosybrown120167273.951.950.4936710.5063291.2980.665.280.3384620002
2AustriaSHOESFOOTBALL GENERICregularmengrayrosybrown120167273.951.950.4936710.5063298.708-6.75-54.00-3.4615380002
3AustriaSHOESFOOTBALL GENERICwideunisexbrownrosybrown020167273.951.950.4936710.5063299.908-7.95-63.60-4.0769230002
4AustriaSHOESINDOORwidewomenlightskybluerosybrown120167273.951.950.4936710.5063295.208-3.25-26.00-1.6666670002
5AustriaSHOESRELAX CASUALregularwomenbluevioletlightblue120167273.951.950.4936710.5063299.608-7.65-61.20-3.9230770002
6AustriaSHOESRUNNINGregularkidsrosybrownlightblue120167273.951.950.4936710.5063299.008-7.05-56.40-3.6153850002
7AustriaSHOESTRAININGslimwomenchocolatelavender120167273.951.950.4936710.50632913.298-11.34-90.72-5.8153850002
8AustriaSHORTSTRAININGregularwomendarkkhakilavender120167273.951.950.4936710.5063292.298-0.34-2.72-0.1743590002
9AustriaSWEATSHIRTSTRAININGwidewomensteelbluelightblue120167273.951.950.4936710.5063294.208-2.25-18.00-1.1538460002